Parallel Flow-Sensitive Pointer Analysis by Graph-Rewriting

Size: px
Start display at page:

Download "Parallel Flow-Sensitive Pointer Analysis by Graph-Rewriting"

Transcription

1 Parae Fow-Sensitive Pointer Anaysis by Grah-Rewriting Vaivaswatha Nagaraj R. Govindarajan Indian Institute of Science, Bangaore PACT 2013

2 2 Outine Introduction Background Fow-sensitive grah-rewriting formuation Imementation and Resuts Concusion

3 3 Outine Introduction Background Fow-sensitive grah-rewriting formuation Imementation and Resuts Concusion

4 4 Fow-sensitive ointer anaysis 1 x = &a y = &b 2 3 z = x z = y z oints-to {?} z oints-to {?} 4

5 5 Fow-sensitive ointer anaysis 1 x = &a y = &b 2 3 z = x x oints-to a y oints-to b z oints-to a x oints-to a y oints-to b z = y 4 x oints-to a y oints-to b z oints-to b

6 Fow-sensitive vs fow-insensitive 1 x = &a y = &b 2 3 z = x x oints-to a y oints-to b z oints-to {a,b} x oints-to a y oints-to b z = y 4 x oints-to a y oints-to b z oints-to {a,b} 6

7 7 Outine Introduction Background Staged fow-sensitive anaysis Grah-rewriting Fow-sensitive grah-rewriting formuation Imementation and Resuts Concusion

8 Staged fow-sensitive ointer anaysis 8 [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] Scaes to arge rograms Faster, ess recise anaysis used to seed u the rimary anaysis

9 Staged fow-sensitive ointer anaysis 9 [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] Scaes to arge rograms Faster, ess recise anaysis used to seed u the rimary anaysis Our goa: Paraeize the staged fow-sensitive ointer anaysis

10 10 Staged fow-sensitive anaysis [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] oints-to {a,b} a oints-to {,q} b oints-to {r,s} 1 2 * = u1 v1 = * w1 = *z1 3 In a traditiona anaysis, oints-to info of both a & b wi be roagated to both 2 and 3

11 11 Staged fow-sensitive anaysis [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] oints-to {a,b} a oints-to {,q} b oints-to {r,s} 1 oints-to {b} 2 * = u1 v1 = * w1 = *z1 z1 oints-to {a} 3

12 12 Staged fow-sensitive anaysis [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] oints-to {a,b} a oints-to {,q} b oints-to {r,s} 1 oints-to {b} 2 z1 oints-to {a} v1 = * w1 = *z1 Points-to info of ony b is required here * = u1 3 Points-to info of ony a is required here

13 13 Staged fow-sensitive anaysis [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] How does staged anaysis work? (1) Use a fast anaysis to get ess recise oints-to information 1 {a,b} z1 {a} {b} w1 {c} 2 * = u1 v1 = * w1 = *z1 3 *w1 = t2 4

14 14 Staged fow-sensitive anaysis [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] How does staged anaysis work? (1) Use a fast anaysis to get ess recise oints-to information (2) Use the ess recise info to find variabes otentiay referenced at indirections 1 * = u1 a1 = (a0) b1 = (b0) {a,b} z1 {a} {b} w1 {c} 2 µ(a1) v1 = * w1 = *z1 µ(b1) *w1 = t2 3 4 c2 = (c1)

15 15 Staged fow-sensitive anaysis [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] How does staged anaysis work? 1 Fow-sensitivity A variabes in SSA form 2 * = u1 a1 = (a0) b1 = (b0) µ(a1) v1 = * w1 = *z1 µ(b1) *w1 = t2 3 4 c2 = (c1)

16 16 Staged fow-sensitive anaysis [Fow-sensitive ointer anaysis for miions of ines of code Ben Hardekof. et a. - CGO'11] How does staged anaysis work? 1 Fow-sensitivity A variabes in SSA form 2 * = u1 a1 = (a0) b1 = (b0) µ(a1) v1 = * w1 = *z1 µ(b1) Proagation of information is thus otimized (3) Buid def-use chains and roagate ony aong these chains *w1 = t2 3 4 c2 = (c1)

17 Paraeizing the staged anaysis Can the staged fow sensitive ointer anaysis be araeized and scaed to mutie cores? 17

18 Towards a arae agorithm # Fow-insensitive ointer anaysis has aready been araeized# Paraeization of fow-insensitive anaysis invoves transforming it to a grah-rewriting robem exose amorhous data-araeism [Parae Incusion-based Points-to Anaysis - Mario Mendez-Lojo, et a. - OOPSLA'10] 18

19 Towards a arae agorithm Fow-insensitive ointer anaysis has aready been araeized# Paraeization of fow-insensitive anaysis invoves transforming it to a grah-rewriting robem exose amorhous data-araeism Our goa: Paraeize the staged fow-sensitive ointer anaysis Formuate it as a grah-rewriting robem # [Parae Incusion-based Points-to Anaysis - Mario Mendez-Lojo, et a. - OOPSLA'10] 19

20 20 Outine Introduction Background Staged fow-sensitive anaysis Grah-rewriting Fow-sensitive grah-rewriting formuation Imementation and Resuts Concusion

21 21 Grah-rewriting [Parae Incusion-based Points-to Anaysis - Mario Mendez-Lojo, et a. - OOPSLA'10] Points to constraint Constraint grah x = &a x y=x x c a y

22 22 Grah-rewriting [Parae Incusion-based Points-to Anaysis - Mario Mendez-Lojo, et a. - OOPSLA'10] Points to constraint Constraint grah x a x = &a x c x y=x a c Ay rewrite-rue y x a c y y Exame: coy rewrite rue

23 23 Outine Introduction Background Fow-sensitive grah-rewriting formuation Imementation and Resuts Concusion

24 24 Grah-rewriting formuation 1 * = u1 a1 = (a0) b1 = (b0) 2 µ(a1) v1 = * w1 = *z1 µ(b1) *w1 = t2 3 4 c2 = (c1) Grah: Nodes: Variabes in the rogram Edges: Points-to, coy, oad, store, etc... Rewrite rues?

25 25 Chaenges Directy using the fow-insensitive grah formuation for fow-sensitive anaysis eads to the foowing chaenges Surious edges eads to imrecision Strong and weak udates at store constraints are not handed

26 26 Chaenges Directy using the fow-insensitive grah formuation for fow-sensitive anaysis eads to the foowing chaenges Surious edges eads to imrecision Soution: otentia edges Strong and weak udates at store constraints are not handed Soution: kique nodes

27 27 Outine Introduction Background Fow-sensitive grah-rewriting formuation Probem of surious edges Handing strong and weak udates Imementation and Resuts Concusion

28 28 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints = &a = *

29 29 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints = &a = * Formuate grah a

30 30 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints = &a = * Formuate grah a a c Ay oad rue

31 31 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints = &a = * Formuate grah Ay oad rue a a c - Loss of recision when used for fow-sensitive anaysis: = * µ(a1) z1 = * µ(a2)

32 32 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints Formuate grah = &a Ay oad rue a = * a c - Loss of recision when used for fow-sensitive anaysis: = * µ(a1) z1 = * µ(a2) a1

33 33 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints Formuate grah = &a Ay oad rue a = * a c - Loss of recision when used for fow-sensitive anaysis: = * µ(a1) z1 = * µ(a2) a1 z1 a2

34 34 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints Formuate grah = &a Ay oad rue a = * a c - Loss of recision when used for fow-sensitive anaysis: = * µ(a1) z1 = * µ(a2) a1 z1 a2

35 35 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints Formuate grah = &a Ay oad rue a = * a c - Loss of recision when used for fow-sensitive anaysis: = * µ(a1) z1 z1 c z1 = * µ(a2) a1 a2 c a1 a2

36 36 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints Formuate grah = &a Ay oad rue a = * a c - Loss of recision when used for fow-sensitive anaysis: = * µ(a1) z1 z1 c z1 = * µ(a2) a1 a2 c a1 Surious edge a2

37 37 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints Formuate grah = &a Ay oad rue a = * a c - Loss of recision when used for fow-sensitive anaysis: = * µ(a1) z1 z1 c z1 = * µ(a2) a1 a2 c a1 Surious edge a2

38 38 Surious edges - Load rue for fow-insensitive anaysis: Points-to constraints Formuate grah = &a Ay oad rue a = * a c - Loss of recision when used for fow-sensitive anaysis: = * µ(a1) c z1 z1 c z1 = * µ(a2) a1 a2 c a1 Surious edge a2

39 Soution: Potentia edges A otentia edge of tye t means that, there coud be an actua edge of tye t, between the same two nodes Potentia edges are added during initia grah construction Some rewrite rues are modified to ook for otentia edges 39

40 40 Soution: Potentia edges - Modified oad rue: Formuate grah = &a = * Modified oad rue a _c a c

41 41 Soution: Potentia edges - Modified oad rue: Formuate grah = &a Modified oad rue a = * a _c c - Modified rue aied to fow-sensitive anaysis: = * µ(a1) z1 _c _c z1 = * µ(a2) a1 a2

42 42 Soution: Potentia edges - Modified oad rue: Formuate grah = &a Modified oad rue = * a a _c c - Modified rue aied to fow-sensitive anaysis: = * µ(a1) _c a1 a2 z1 _c z1 = * µ(a2) z1 c c a1 a2

43 43 Outine Introduction Background Fow-sensitive grah-rewriting formuation Probem of surious edges Handing strong and weak udates Imementation and Resuts Concusion

44 Strong and weak udates As the fow sensitive anaysis rogresses: * = u1 a1 = (a0) b1 = (b0) 44

45 Strong and weak udates As the fow sensitive anaysis rogresses: How to udate a1? * = u1 a1 = (a0) b1 = (b0) 45

46 Strong and weak udates As the fow sensitive anaysis rogresses: How to udate a1? * = u1 a1 = (a0) b1 = (b0) oints-to {a1} 46

47 Strong and weak udates As the fow sensitive anaysis rogresses: How to udate a1? * = u1 a1 = (a0) b1 = (b0) Strong Udate oints-to {a1} a 1 u1 47

48 48 Strong and weak udates As the fow sensitive anaysis rogresses: How to udate a1? * = u1 a1 = (a0) b1 = (b0) Strong Udate oints-to {a1} oints-to {a1,b1} a 1 u1

49 49 Strong and weak udates As the fow sensitive anaysis rogresses: How to udate a1? Strong Udate oints-to {a1} a 1 u1 * = u1 a1 = (a0) b1 = (b0) Weak Udate oints-to {a1,b1} a1 u1 a0

50 50 Strong and weak udates * = u1 a1 = (a0) b1 = (b0) udate a1 as if oints to a then a1 u1 end if if oints to b then a1 a0 end if

51 51 Strong and weak udates Points-to set of a deends 1 on whether oints-to * = u1 a1 = (a0) b1 = (b0) b1 udate a1 as if oints to a then a1 u1 end if if oints to b then a1 a0 end if

52 52 Strong and weak udates Points-to set of a deends 1 on whether oints-to * = u1 b1 udate a1 as a1 = (a0) b1 = (b0) if oints to b then a1 a0 end if Connect a and 1 if oints to a then a1 u1 end if b1

53 Strong and weak udates * = u1 a1 = (a0) b1 = (b0) Soution: Connect a variabes on the LHS of, within a store. Use a kique node as the common connection. a1 k b1 1-1 kique node store constraint 53

54 54 Strong and weak udates * = u1 a1 = (a0) b1 = (b0) udate a1 as if oints to a then a1 u1 end if if oints to b then a1 a0 end if

55 55 Strong and weak udates * = u1 if oints to a then a1 u1 end if udate a1 as a1 = (a0) b1 = (b0) if oints to b then a1 a0 end if Rewrite rue to udate a : 1 a1 s a1 s _c u1 c u1

56 56 Strong and weak udates * = u1 a1 = (a0) b1 = (b0) udate a1 as if oints to a then a1 u1 end if if oints to b then a1 a0 end if

57 57 Strong and weak udates * = u1 udate a1 as a1 = (a0) b1 = (b0) if oints to a then a1 u1 end if if oints to b then a1 a0 end if Rewrite rue to udate a : (2 stes) 1 k1 b1 b1 Note: The rewrite rue shown here is not comete.

58 58 Strong and weak udates * = u1 if oints to a then a1 u1 end if udate a1 as a1 = (a0) b1 = (b0) if oints to b then a1 a0 end if Rewrite rue to udate a : (2 stes) 1 b1 k1 b1 k1 k1 b1 a0 c a1 Note: The rewrite rue shown here is not comete.

59 59 Strong and weak udates * = u1 a1 = (a0) b1 = (b0) udate a1 as if oints to a then a1 u1 end if if oints to b then a1 a0 end if Handed

60 Aying the rewrite rues Rewrite rues can be aied in any order, and to any node Rewrite rues are aied unti fixed oint At any time, there may be mutie nodes ready for rewrite rue aication, aowing arae aication of rewrite rues 60

61 61 Outine Introduction Background Fow-sensitive grah-rewriting formuation Imementation and Resuts Concusion

62 Imementation detais Inte threading buiding bocks (TBB) used to manage arae workoad A dua-workist based aroach to kee track of nodes for rocessing A hash tabe based concurrent data structure (concurrent_unordered_set), rovided by TBB used to reresent grah edges 62

63 Machine configuration 4-socket machine 2.0 GHz, 8-core rocessor on each socket 64GB memory Debian GNU/Linux 6.0 Inte Threading Buiding Bocks

64 64 Benchmarks Benchmark Number of grah nodes Larger (ines of code) rograms from SPEC k - 414k Ex text rocessor 19k Nethack text based game 222k Sendmai emai server 71k SVN revision contro system 6439k Vim text editor 1265k A of these have been used in revious ointer anaysis exeriments

65 65 Scaing

66 66 Outine Introduction Background Fow-sensitive grah-rewriting formuation Imementation and Resuts Concusion

67 67 Concusion First araeization of recise fow-sensitive ointer anaysis Fow-sensitive ointer anaysis as a grahrewriting robem easy to take advantage of amorhous data araeism Scaing of u to 6.9x, for 8 threads shown

68 68 Acknowedgement PACT student trave grant GARP student grant - IISc Ben Hardekof and Mario Mendez-Lojo Ruesh Nasre HPC ab members SERC IISc

69 69 Thank you Questions?

70 x 08 x Backu: recent resuts combine workists thread 2 threads 4 threads 6 threads 8 threads seedu ex 254.ga 176.gcc nethack 253.er 197.arser sendmai vim svn 255.vortex

71 Backu: Comarison with SFS 71

72 72 Backu: Program size

73 Backu: Average workist size (a).. (h) are workists for each rewrite rue 73

74 Backu: - Future work Exore different orders of aying rewrite rues Reordering nodes for ocaity might give better scaing Extend for context-sensitivity Using concurrent sarse-bit-vectors for reresenting edges may imrove erformance 74

Near-optimal Delay Constrained Routing in Virtual Circuit Networks

Near-optimal Delay Constrained Routing in Virtual Circuit Networks Near-otima Deay Constrained Routing in Virtua Circuit Netorks Hong-Hsu Yen and Frank Yeong-Sung Lin Deartment of Information Management Nationa Taian University Taiei, Taian, R.O.C. Te: +886--68 Fa: +886--58

More information

Pointer Analysis. What is Points-to Analysis? Outline. What is Points-to Analysis? What is Points-to Analysis? What is Pointer Analysis? Rupesh Nasre.

Pointer Analysis. What is Points-to Analysis? Outline. What is Points-to Analysis? What is Points-to Analysis? What is Pointer Analysis? Rupesh Nasre. Pointer Analysis What is? Ruesh Nasre. CS6843 Analysis IIT Madras Jan 2016 = a; if ( == *) { } else { } a oints to x 4 Outline What is? Introduction Pointer analysis as a DFA rolem Design decisions analysis,

More information

THE MOLDFLOW ANALYSE OF THE INJECTION PARTS, AND THE IMPROVEMENT OF THE INJECTION PROCESS.

THE MOLDFLOW ANALYSE OF THE INJECTION PARTS, AND THE IMPROVEMENT OF THE INJECTION PROCESS. THE MOLDFLOW ANALYSE OF THE INJECTION PARTS, AND THE IMPROVEMENT OF THE INJECTION PROCESS. Togan V.C. 1,2, Ioniţă Gh. 1 1 Vaahia University, Facuty of Materia Engineering, Mechatronics and Robotics, 18-24,

More information

Equality-Based Translation Validator for LLVM

Equality-Based Translation Validator for LLVM Equality-Based Translation Validator for LLVM Michael Ste, Ross Tate, and Sorin Lerner University of California, San Diego {mste,rtate,lerner@cs.ucsd.edu Abstract. We udated our Peggy tool, reviously resented

More information

Pointer Analysis. Outline: What is pointer analysis Intraprocedural pointer analysis Interprocedural pointer analysis. Andersen and Steensgaard

Pointer Analysis. Outline: What is pointer analysis Intraprocedural pointer analysis Interprocedural pointer analysis. Andersen and Steensgaard Poiner anaysis Poiner Anaysis Ouine: Wha is oiner anaysis Inrarocedura oiner anaysis Inerrocedura oiner anaysis Andersen and Seensgaard Poiner and Aias Anaysis Aiases: wo exressions ha denoe he same memory

More information

Neural Network Enhancement of the Los Alamos Force Deployment Estimator

Neural Network Enhancement of the Los Alamos Force Deployment Estimator Missouri University of Science and Technoogy Schoars' Mine Eectrica and Computer Engineering Facuty Research & Creative Works Eectrica and Computer Engineering 1-1-1994 Neura Network Enhancement of the

More information

A Local Optimal Method on DSA Guiding Template Assignment with Redundant/Dummy Via Insertion

A Local Optimal Method on DSA Guiding Template Assignment with Redundant/Dummy Via Insertion A Loca Optima Method on DSA Guiding Tempate Assignment with Redundant/Dummy Via Insertion Xingquan Li 1, Bei Yu 2, Jiani Chen 1, Wenxing Zhu 1, 24th Asia and South Pacific Design T h e p i c Automation

More information

Non-Lecture N: Convex Hulls

Non-Lecture N: Convex Hulls N Convex Hus N.1 Definitions We are given a set P of n oints in the ane. We want to comute something caed the convex hu of P. Intuitivey, the convex hu is what you get by driving a nai into the ane at

More information

An Alternative Approach for Solving Bi-Level Programming Problems

An Alternative Approach for Solving Bi-Level Programming Problems American Journa of Operations Research, 07, 7, 9-7 http://www.scirp.org/ourna/aor ISSN Onine: 60-889 ISSN rint: 60-880 An Aternative Approach for Soving Bi-Leve rogramming robems Rashmi Bira, Viay K. Agarwa,

More information

Fuzzy Estimations of Process Incapability Index

Fuzzy Estimations of Process Incapability Index Fuzzy Estimations of Process Incaabiity Index engiz Kahraman Ihsan Kaya Abstract Process caabiity indices (PIs) rovide numerica measures on whether a rocess confirms to the defined manufacturing caabiity

More information

Formulation of Loss minimization Problem Using Genetic Algorithm and Line-Flow-based Equations

Formulation of Loss minimization Problem Using Genetic Algorithm and Line-Flow-based Equations Formuation of Loss minimization Probem Using Genetic Agorithm and Line-Fow-based Equations Sharanya Jaganathan, Student Member, IEEE, Arun Sekar, Senior Member, IEEE, and Wenzhong Gao, Senior member, IEEE

More information

Design of IP Networks with End-to. to- End Performance Guarantees

Design of IP Networks with End-to. to- End Performance Guarantees Design of IP Networks with End-to to- End Performance Guarantees Irena Atov and Richard J. Harris* ( Swinburne University of Technoogy & *Massey University) Presentation Outine Introduction Mutiservice

More information

The Rest of the Course More DFA and Model Checking

The Rest of the Course More DFA and Model Checking The Rest of the Course More DFA and Model Checking Tuesday, Dec. 6: Guest Lecture Thursday, Dec. 8: Guest Lecture Final Exam Wednesday, Dec. 14 @ 10:30-12:30 Prof. Leon Osterweil CS 521-621 Fall Semester

More information

BGP-Based SPF IETF 96, Berlin. Keyur Patel, Cisco Acee Lindem, Cisco Derek Yeung, Cisco Abhay Roy, Cisco Venu Venugopal, Cisco

BGP-Based SPF IETF 96, Berlin. Keyur Patel, Cisco Acee Lindem, Cisco Derek Yeung, Cisco Abhay Roy, Cisco Venu Venugopal, Cisco BGP-Based SPF IETF 96, Berin Keyur Pate, Cisco Acee Lindem, Cisco Derek Yeung, Cisco Abhay Roy, Cisco Venu Venugopa, Cisco 1 Data Center Routing Routing Probem Space Routing scaing for Massivey Scaabe

More information

Query-Directed Adaptive Heap Cloning for Optimizing Compilers

Query-Directed Adaptive Heap Cloning for Optimizing Compilers Query-Directed Adative Hea Cloning for Otimizing Comilers Yulei Sui Yue Li Jingling Xue Programming Languages and Comilers Grou School of Comuter Science and Engineering University of New South Wales,

More information

CSE120 Principles of Operating Systems. Prof Yuanyuan (YY) Zhou Scheduling

CSE120 Principles of Operating Systems. Prof Yuanyuan (YY) Zhou Scheduling CSE120 Principes of Operating Systems Prof Yuanyuan (YY) Zhou Scheduing Announcement Homework 2 due on October 25th Project 1 due on October 26th 2 CSE 120 Scheduing and Deadock Scheduing Overview In discussing

More information

Simple example. Analysis of programs with pointers. Points-to relation. Program model. Points-to graph. Ordering on points-to relation

Simple example. Analysis of programs with pointers. Points-to relation. Program model. Points-to graph. Ordering on points-to relation Simle eamle Analsis of rograms with ointers := 5 tr := @ *tr := 9 := rogram S1 S2 S3 S4 deendences What are the deendences in this rogram? Problem: just looking at variable names will not give ou the correct

More information

A Spatio-Temporal Fuzzy Logic System for Process Control

A Spatio-Temporal Fuzzy Logic System for Process Control Proceedings of the 2009 IEEE Internationa Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009 1 A Satio-Temora Fuy Logic System for Process Contro Han-Xiong Li Schoo of Mechanica

More information

CylanceOPTICS. Frequently Asked Questions

CylanceOPTICS. Frequently Asked Questions CyanceOPTICS Frequenty Asked Questions Question What is CyanceOPTICS? CyanceOPTICS is an AI driven endpoint detection and response component providing consistent visibiity, root cause anaysis, scaabe threat

More information

Distance Weighted Discrimination and Second Order Cone Programming

Distance Weighted Discrimination and Second Order Cone Programming Distance Weighted Discrimination and Second Order Cone Programming Hanwen Huang, Xiaosun Lu, Yufeng Liu, J. S. Marron, Perry Haaand Apri 3, 2012 1 Introduction This vignette demonstrates the utiity and

More information

Topology-aware Key Management Schemes for Wireless Multicast

Topology-aware Key Management Schemes for Wireless Multicast Topoogy-aware Key Management Schemes for Wireess Muticast Yan Sun, Wade Trappe,andK.J.RayLiu Department of Eectrica and Computer Engineering, University of Maryand, Coege Park Emai: ysun, kjriu@gue.umd.edu

More information

A Design Method for Optimal Truss Structures with Certain Redundancy Based on Combinatorial Rigidity Theory

A Design Method for Optimal Truss Structures with Certain Redundancy Based on Combinatorial Rigidity Theory 0 th Word Congress on Structura and Mutidiscipinary Optimization May 9 -, 03, Orando, Forida, USA A Design Method for Optima Truss Structures with Certain Redundancy Based on Combinatoria Rigidity Theory

More information

Identity-sensitive Points-to Analysis for the Dynamic Behavior of JavaScript Objects

Identity-sensitive Points-to Analysis for the Dynamic Behavior of JavaScript Objects Identity-sensitive Points-to Analysis for the Dynamic Behavior of JavaScrit Objects Shiyi Wei and Barbara G. Ryder Deartment of Comuter Science, Virginia Tech, Blacksburg, VA, USA. {wei,ryder}@cs.vt.edu

More information

Probabilistic Graphical Models

Probabilistic Graphical Models Schoo of Computer Science Probabiistic Graphica Modes Distributed Agorithms for ML David (Wei) Dai Lecture 21, Apri 5, 2017 Eric Xing @ CMU, 2005-2017 1 Massive Data 1B+ USERS 30+ PETABYTES 32 miion pages

More information

AUTOMATIC PARAMETER ESTIMATION IN MRF BASED SUPER RESOLUTION MAPPING

AUTOMATIC PARAMETER ESTIMATION IN MRF BASED SUPER RESOLUTION MAPPING AUTOMATIC PARAMETER ESTIMATION IN MRF BASED SUPER RESOLUTION MAPPING ANTENEH LEMMI ESHETE March, 0 SUPERVISORS: Dr. V. A. Toekin Dr. N. A. S. Hamm AUTOMATIC PARAMETER ESTIMATION IN MRF BASED SUPER RESOLUTION

More information

An Indexing Framework for Structured P2P Systems

An Indexing Framework for Structured P2P Systems An Indexing Framework for Structured P2P Systems Adina Crainiceanu Prakash Linga Ashwin Machanavajjhala Johannes Gehrke Carl Lagoze Jayavel Shanmugasundaram Deartment of Comuter Science, Cornell University

More information

Lecture outline Graphics and Interaction Scan Converting Polygons and Lines. Inside or outside a polygon? Scan conversion.

Lecture outline Graphics and Interaction Scan Converting Polygons and Lines. Inside or outside a polygon? Scan conversion. Lecture outine 433-324 Graphics and Interaction Scan Converting Poygons and Lines Department of Computer Science and Software Engineering The Introduction Scan conversion Scan-ine agorithm Edge coherence

More information

Chapter 8: Adaptive Networks

Chapter 8: Adaptive Networks Chater : Adative Networks Introduction (.1) Architecture (.2) Backroagation for Feedforward Networks (.3) Jyh-Shing Roger Jang et al., Neuro-Fuzzy and Soft Comuting: A Comutational Aroach to Learning and

More information

Mobile App Recommendation: Maximize the Total App Downloads

Mobile App Recommendation: Maximize the Total App Downloads Mobie App Recommendation: Maximize the Tota App Downoads Zhuohua Chen Schoo of Economics and Management Tsinghua University chenzhh3.12@sem.tsinghua.edu.cn Yinghui (Catherine) Yang Graduate Schoo of Management

More information

Multi-Parametric Online RWA based on Impairment Generating Sources

Multi-Parametric Online RWA based on Impairment Generating Sources Muti-Parametric Onine RWA based on Imairment Generating Sources P. Kokkinos, K. Christodouoouos, K. Manousakis, E. A. Varvarigos Comuter Engineering and Informatics Deartment, University of Patras, Greece,

More information

Load Balancing by MPLS in Differentiated Services Networks

Load Balancing by MPLS in Differentiated Services Networks Load Baancing by MPLS in Differentiated Services Networks Riikka Susitaiva, Jorma Virtamo, and Samui Aato Networking Laboratory, Hesinki University of Technoogy P.O.Box 3000, FIN-02015 HUT, Finand {riikka.susitaiva,

More information

Proceedings of the International Conference on Systolic Arrays, San Diego, California, U.S.A., May 25-27, 1988 AN EFFICIENT ASYNCHRONOUS MULTIPLIER!

Proceedings of the International Conference on Systolic Arrays, San Diego, California, U.S.A., May 25-27, 1988 AN EFFICIENT ASYNCHRONOUS MULTIPLIER! [1,2] have, in theory, revoutionized cryptography. Unfortunatey, athough offer many advantages over conventiona and authentication), such cock synchronization in this appication due to the arge operand

More information

Fastest-Path Computation

Fastest-Path Computation Fastest-Path Computation DONGHUI ZHANG Coege of Computer & Information Science Northeastern University Synonyms fastest route; driving direction Definition In the United states, ony 9.% of the househods

More information

Modern Physical Design: Algorithm Technology Methodology (Part III)

Modern Physical Design: Algorithm Technology Methodology (Part III) ICCAD Tutoria, November 9, 2000 Modern Physica Design: Agorithm Technoogy Methodoogy (Part III) Stan Chow Ammocore Andrew B. Kahng UCSD Majid Sarrafzadeh UCLA 1 ICCAD Tutoria: November 9, 2000 Goas of

More information

A Two-Step Adaptive Error Recovery Scheme for Video Transmission over Wireless Networks

A Two-Step Adaptive Error Recovery Scheme for Video Transmission over Wireless Networks A Two-Ste Adative Error Recovery Scheme for Video Transmission over Wireess Networks Daji Qiao and Kang G. Shin Rea-Time Comuting Laboratory Deartment of Eectrica Engineering and Comuter Science The University

More information

Application of Intelligence Based Genetic Algorithm for Job Sequencing Problem on Parallel Mixed-Model Assembly Line

Application of Intelligence Based Genetic Algorithm for Job Sequencing Problem on Parallel Mixed-Model Assembly Line American J. of Engineering and Appied Sciences 3 (): 5-24, 200 ISSN 94-7020 200 Science Pubications Appication of Inteigence Based Genetic Agorithm for Job Sequencing Probem on Parae Mixed-Mode Assemby

More information

Chapter Multidimensional Direct Search Method

Chapter Multidimensional Direct Search Method Chapter 09.03 Mutidimensiona Direct Search Method After reading this chapter, you shoud be abe to:. Understand the fundamentas of the mutidimensiona direct search methods. Understand how the coordinate

More information

JOINT IMAGE REGISTRATION AND EXAMPLE-BASED SUPER-RESOLUTION ALGORITHM

JOINT IMAGE REGISTRATION AND EXAMPLE-BASED SUPER-RESOLUTION ALGORITHM JOINT IMAGE REGISTRATION AND AMPLE-BASED SUPER-RESOLUTION ALGORITHM Hyo-Song Kim, Jeyong Shin, and Rae-Hong Park Department of Eectronic Engineering, Schoo of Engineering, Sogang University 35 Baekbeom-ro,

More information

Replication of Virtual Network Functions: Optimizing Link Utilization and Resource Costs

Replication of Virtual Network Functions: Optimizing Link Utilization and Resource Costs Repication of Virtua Network Functions: Optimizing Link Utiization and Resource Costs Francisco Carpio, Wogang Bziuk and Admea Jukan Technische Universität Braunschweig, Germany Emai:{f.carpio, w.bziuk,

More information

Proceedings of 3DPVT'08 - the Fourth International Symposium on 3D Data Processing, Visualization and Transmission

Proceedings of 3DPVT'08 - the Fourth International Symposium on 3D Data Processing, Visualization and Transmission Proceedings of 3PVT'08 - the Fourth Internationa Symosium on 3 ata Processing, Visuaization and Transmission MRF Stereo with Statistica Parameter stimation Shafik Hu, Andreas Koschan, Besma Abidi, and

More information

Mitigating the Impact of Decompression Latency in L1 Compressed Data Caches via Prefetching

Mitigating the Impact of Decompression Latency in L1 Compressed Data Caches via Prefetching Mitigating the Imact of Decomression Latency in L1 Comressed Data Caches via Prefetching by Sean Rea A thesis resented to Lakehead University in artial fulfillment of the requirement for the degree of

More information

10 File System Mass Storage Structure Mass Storage Systems Mass Storage Structure Mass Storage Structure FILE SYSTEM 1

10 File System Mass Storage Structure Mass Storage Systems Mass Storage Structure Mass Storage Structure FILE SYSTEM 1 10 File System 1 We will examine this chater in three subtitles: Mass Storage Systems OERATING SYSTEMS FILE SYSTEM 1 File System Interface File System Imlementation 10.1.1 Mass Storage Structure 3 2 10.1

More information

Outline. Parallel Numerical Algorithms. Forward Substitution. Triangular Matrices. Solving Triangular Systems. Back Substitution. Parallel Algorithm

Outline. Parallel Numerical Algorithms. Forward Substitution. Triangular Matrices. Solving Triangular Systems. Back Substitution. Parallel Algorithm Outine Parae Numerica Agorithms Chapter 8 Prof. Michae T. Heath Department of Computer Science University of Iinois at Urbana-Champaign CS 554 / CSE 512 1 2 3 4 Trianguar Matrices Michae T. Heath Parae

More information

Stereo. Stereo: 3D from Two Views. Stereo Correspondence. Fundamental Matrix. Fundamental Matrix

Stereo. Stereo: 3D from Two Views. Stereo Correspondence. Fundamental Matrix. Fundamental Matrix Stereo: 3D from wo Views Stereo scene oint otica center image ane Basic rincie: rianguation Gives reconstruction as intersection of two ras equires caibration oint corresondence Stereo Corresondence Determine

More information

PREDICTING LINKS IN LARGE COAUTHORSHIP NETWORKS

PREDICTING LINKS IN LARGE COAUTHORSHIP NETWORKS PREDICTING LINKS IN LARGE COAUTHORSHIP NETWORKS Kevin Miller, Vivian Lin, and Rui Zhang Grou ID: 5 1. INTRODUCTION The roblem we are trying to solve is redicting future links or recovering missing links

More information

Space-Time Trade-offs.

Space-Time Trade-offs. Space-Time Trade-offs. Chethan Kamath 03.07.2017 1 Motivation An important question in the study of computation is how to best use the registers in a CPU. In most cases, the amount of registers avaiabe

More information

Language Identification for Texts Written in Transliteration

Language Identification for Texts Written in Transliteration Language Identification for Texts Written in Transiteration Andrey Chepovskiy, Sergey Gusev, Margarita Kurbatova Higher Schoo of Economics, Data Anaysis and Artificia Inteigence Department, Pokrovskiy

More information

A New and Efficient Algorithm-Based Fault Tolerance Scheme for A Million Way Parallelism

A New and Efficient Algorithm-Based Fault Tolerance Scheme for A Million Way Parallelism A New and Efficient Algorithm-Based Fault Tolerance Scheme for A Million Way Parallelism Erlin Yao, Mingyu Chen, Rui Wang, Wenli Zhang, Guangming Tan Key Laboratory of Comuter System and Architecture Institute

More information

GPU Implementation of Parallel SVM as Applied to Intrusion Detection System

GPU Implementation of Parallel SVM as Applied to Intrusion Detection System GPU Impementation of Parae SVM as Appied to Intrusion Detection System Sudarshan Hiray Research Schoar, Department of Computer Engineering, Vishwakarma Institute of Technoogy, Pune, India sdhiray7@gmai.com

More information

A Memory Grouping Method for Sharing Memory BIST Logic

A Memory Grouping Method for Sharing Memory BIST Logic A Memory Grouping Method for Sharing Memory BIST Logic Masahide Miyazai, Tomoazu Yoneda, and Hideo Fuiwara Graduate Schoo of Information Science, Nara Institute of Science and Technoogy (NAIST), 8916-5

More information

Operations on Singly (Simply) Linked Lists

Operations on Singly (Simply) Linked Lists LEC. 4 College of Information Technology / Software Deartment.. Data Structures / Second Class / 2016-2017 InsertFirst Oerations on Singly (Simly) Linked Lists The insertfirst() method of LinkList inserts

More information

Further Optimization of the Decoding Method for Shortened Binary Cyclic Fire Code

Further Optimization of the Decoding Method for Shortened Binary Cyclic Fire Code Further Optimization of the Decoding Method for Shortened Binary Cycic Fire Code Ch. Nanda Kishore Heosoft (India) Private Limited 8-2-703, Road No-12 Banjara His, Hyderabad, INDIA Phone: +91-040-3378222

More information

CS 470 Spring Mike Lam, Professor. Performance Analysis

CS 470 Spring Mike Lam, Professor. Performance Analysis CS 470 Sring 2018 Mike Lam, Professor Performance Analysis Performance analysis Why do we arallelize our rograms? Performance analysis Why do we arallelize our rograms? So that they run faster! Performance

More information

Joint Optimization of Intra- and Inter-Autonomous System Traffic Engineering

Joint Optimization of Intra- and Inter-Autonomous System Traffic Engineering Joint Optimization of Intra- and Inter-Autonomous System Traffic Engineering Kin-Hon Ho, Michae Howarth, Ning Wang, George Pavou and Styianos Georgouas Centre for Communication Systems Research, University

More information

Lecture 18. Today, we will discuss developing algorithms for a basic model for parallel computing the Parallel Random Access Machine (PRAM) model.

Lecture 18. Today, we will discuss developing algorithms for a basic model for parallel computing the Parallel Random Access Machine (PRAM) model. U.C. Berkeley CS273: Parallel and Distributed Theory Lecture 18 Professor Satish Rao Lecturer: Satish Rao Last revised Scribe so far: Satish Rao (following revious lecture notes quite closely. Lecture

More information

Big Data for Data Science

Big Data for Data Science Big Data for Data Science SQL on Big Data www.cwi.n/~boncz/bads THE DEBATE: DATABASE SYSTEMS VS MAPREDUCE www.cwi.n/~boncz/bads A major step backwards? MapReduce is a step backward in database access Schemas

More information

An Optimizing Compiler

An Optimizing Compiler An Optimizing Compier The big difference between interpreters and compiers is that compiers have the abiity to think about how to transate a source program into target code in the most effective way. Usuay

More information

Shape Analysis with Structural Invariant Checkers

Shape Analysis with Structural Invariant Checkers Shape Anaysis with Structura Invariant Checkers Bor-Yuh Evan Chang Xavier Riva George C. Necua University of Caifornia, Berkeey SAS 2007 Exampe: Typestate with shape anaysis Concrete Exampe Abstraction

More information

AN EVOLUTIONARY APPROACH TO OPTIMIZATION OF A LAYOUT CHART

AN EVOLUTIONARY APPROACH TO OPTIMIZATION OF A LAYOUT CHART 13 AN EVOLUTIONARY APPROACH TO OPTIMIZATION OF A LAYOUT CHART Eva Vona University of Ostrava, 30th dubna st. 22, Ostrava, Czech Repubic e-mai: Eva.Vona@osu.cz Abstract: This artice presents the use of

More information

An Exponential Time 2-Approximation Algorithm for Bandwidth

An Exponential Time 2-Approximation Algorithm for Bandwidth An Exponentia Time 2-Approximation Agorithm for Bandwidth Martin Fürer 1, Serge Gaspers 2, Shiva Prasad Kasiviswanathan 3 1 Computer Science and Engineering, Pennsyvania State University, furer@cse.psu.edu

More information

Human Instance Segmentation from Video using Detector-based Conditional Random Fields

Human Instance Segmentation from Video using Detector-based Conditional Random Fields VINEET ET AL.: HUMAN INSTANCE SEGMENTATION 1 Human Instance Segmentation from Video using Detector-based Conditiona Random Fieds Vibhav Vineet 1 vibhav.vineet-2010@brookes.ac.uk Jonathan Warre 1 jwarre@brookes.ac.uk

More information

Split Restoration with Wavelength Conversion in WDM Networks*

Split Restoration with Wavelength Conversion in WDM Networks* Spit Reoration with aveength Conversion in DM Networks* Yuanqiu Luo and Nirwan Ansari Advanced Networking Laborator Department of Eectrica and Computer Engineering New Jerse Initute of Technoog Universit

More information

RDF Objects 1. Alex Barnell Information Infrastructure Laboratory HP Laboratories Bristol HPL November 27 th, 2002*

RDF Objects 1. Alex Barnell Information Infrastructure Laboratory HP Laboratories Bristol HPL November 27 th, 2002* RDF Objects 1 Aex Barne Information Infrastructure Laboratory HP Laboratories Bristo HPL-2002-315 November 27 th, 2002* E-mai: Andy_Seaborne@hp.hp.com RDF, semantic web, ontoogy, object-oriented datastructures

More information

Extracting semistructured data from the Web: An XQuery Based Approach

Extracting semistructured data from the Web: An XQuery Based Approach EurAsia-ICT 2002, Shiraz-Iran, 29-31 Oct. Extracting semistructured data from the Web: An XQuery Based Approach Gies Nachouki Université de Nantes - Facuté des Sciences, IRIN, 2, rue de a Houssinière,

More information

Using Standard AADL for COMPASS

Using Standard AADL for COMPASS Using Standard AADL for COMPASS (noll@cs.rwth-aachen.de) AADL Standards Meeting Aachen, Germany; July 5 8, 06 Overview Introduction SLIM Language Udates COMPASS Develoment Roadma Fault Injections Parametric

More information

CLOUD RADIO ACCESS NETWORK WITH OPTIMIZED BASE-STATION CACHING

CLOUD RADIO ACCESS NETWORK WITH OPTIMIZED BASE-STATION CACHING CLOUD RADIO ACCESS NETWORK WITH OPTIMIZED BASE-STATION CACHING Binbin Dai and Wei Yu Ya-Feng Liu Department of Eectrica and Computer Engineering University of Toronto, Toronto ON, Canada M5S 3G4 Emais:

More information

understood as processors that match AST patterns of the source language and translate them into patterns in the target language.

understood as processors that match AST patterns of the source language and translate them into patterns in the target language. A Basic Compier At a fundamenta eve compiers can be understood as processors that match AST patterns of the source anguage and transate them into patterns in the target anguage. Here we wi ook at a basic

More information

Discrete elastica model for shape design of grid shells

Discrete elastica model for shape design of grid shells Abstracts for IASS Annua Symposium 017 5 8th September, 017, Hamburg, Germany Annette Böge, Manfred Grohmann (eds.) Discrete eastica mode for shape design of grid shes Yusuke SAKAI* and Makoto OHSAKI a

More information

Endoscopic Motion Compensation of High Speed Videoendoscopy

Endoscopic Motion Compensation of High Speed Videoendoscopy Endoscopic Motion Compensation of High Speed Videoendoscopy Bharath avuri Department of Computer Science and Engineering, University of South Caroina, Coumbia, SC - 901. ravuri@cse.sc.edu Abstract. High

More information

Meeting Exchange 4.1 Service Pack 2 Release Notes for the S6200/S6800 Servers

Meeting Exchange 4.1 Service Pack 2 Release Notes for the S6200/S6800 Servers Meeting Exchange 4.1 Service Pack 2 Reease Notes for the S6200/S6800 Servers The Meeting Exchange S6200/S6800 Media Servers are SIP-based voice and web conferencing soutions that extend Avaya s conferencing

More information

Intro to Programming & C Why Program? 1.2 Computer Systems: Hardware and Software. Why Learn to Program?

Intro to Programming & C Why Program? 1.2 Computer Systems: Hardware and Software. Why Learn to Program? Intro to Programming & C++ Unit 1 Sections 1.1-3 and 2.1-10, 2.12-13, 2.15-17 CS 1428 Spring 2018 Ji Seaman 1.1 Why Program? Computer programmabe machine designed to foow instructions Program a set of

More information

Sample of a training manual for a software tool

Sample of a training manual for a software tool Sampe of a training manua for a software too We use FogBugz for tracking bugs discovered in RAPPID. I wrote this manua as a training too for instructing the programmers and engineers in the use of FogBugz.

More information

Forgot to compute the new centroids (-1); error in centroid computations (-1); incorrect clustering results (-2 points); more than 2 errors: 0 points.

Forgot to compute the new centroids (-1); error in centroid computations (-1); incorrect clustering results (-2 points); more than 2 errors: 0 points. Probem 1 a. K means is ony capabe of discovering shapes that are convex poygons [1] Cannot discover X shape because X is not convex. [1] DBSCAN can discover X shape. [1] b. K-means is prototype based and

More information

Backing-up Fuzzy Control of a Truck-trailer Equipped with a Kingpin Sliding Mechanism

Backing-up Fuzzy Control of a Truck-trailer Equipped with a Kingpin Sliding Mechanism Backing-up Fuzzy Contro of a Truck-traier Equipped with a Kingpin Siding Mechanism G. Siamantas and S. Manesis Eectrica & Computer Engineering Dept., University of Patras, Patras, Greece gsiama@upatras.gr;stam.manesis@ece.upatras.gr

More information

EE 122 Final Exam - Solution Date: December 14, 2002

EE 122 Final Exam - Solution Date: December 14, 2002 Name: SID: ee ogin: Day/Time of section you atten: EE Fina Exam - Soution Date: December 4, 00 Probem Points /0 /0 3 /0 4 /0 5 /0 6 /0 7 /0 8 /0 9 /0 0 /0 Tota /00 . Question (0 pt) (a) (5 pt) Expain briefy

More information

A Robust Sign Language Recognition System with Sparsely Labeled Instances Using Wi-Fi Signals

A Robust Sign Language Recognition System with Sparsely Labeled Instances Using Wi-Fi Signals A Robust Sign Language Recognition System with Sparsey Labeed Instances Using Wi-Fi Signas Jiacheng Shang, Jie Wu Center for Networked Computing Dept. of Computer and Info. Sciences Tempe University Motivation

More information

MULTIGRID REDUCTION IN TIME FOR NONLINEAR PARABOLIC PROBLEMS: A CASE STUDY

MULTIGRID REDUCTION IN TIME FOR NONLINEAR PARABOLIC PROBLEMS: A CASE STUDY MULTIGRID REDUCTION IN TIME FOR NONLINEAR PARABOLIC PROBLEMS: A CASE STUDY R.D. FALGOUT, T.A. MANTEUFFEL, B. O NEILL, AND J.B. SCHRODER Abstract. The need for paraeism in the time dimension is being driven

More information

index.pdf March 17,

index.pdf March 17, index.pdf March 17, 2013 1 ITI 1121. Introduction to omputing II Marce Turcotte Schoo of Eectrica Engineering and omputer Science Linked List (Part 2) Tai pointer ouby inked ist ummy node Version of March

More information

Delay Budget Partitioning to Maximize Network Resource Usage Efficiency

Delay Budget Partitioning to Maximize Network Resource Usage Efficiency Deay Budget Partitioning to Maximize Network Resource Usage Efficiency Kartik Gopaan Tzi-cker Chiueh Yow-Jian Lin Forida State University Stony Brook University Tecordia Technoogies kartik@cs.fsu.edu chiueh@cs.sunysb.edu

More information

Loop Closing for Visual Pose Tracking during Close-Range 3-D Modeling

Loop Closing for Visual Pose Tracking during Close-Range 3-D Modeling Loo Cosing for Visua Pose Tracking during Cose-Range 3-D Modeing Kaus H Strob Robotics and Mechatronics Center (RMC) German Aerosace Center (DLR) D-82234 Wessing, Germany Abstract This work deas with the

More information

(0,l) (0,0) (l,0) (l,l)

(0,l) (0,0) (l,0) (l,l) Parae Domain Decomposition and Load Baancing Using Space-Fiing Curves Srinivas Auru Fatih E. Sevigen Dept. of CS Schoo of EECS New Mexico State University Syracuse University Las Cruces, NM 88003-8001

More information

IBM Research Report. On the Tradeoff among Capacity, Routing Hops, and Being Peer-to-Peer in the Design of Structured Overlay Networks

IBM Research Report. On the Tradeoff among Capacity, Routing Hops, and Being Peer-to-Peer in the Design of Structured Overlay Networks RC355 (W5-4) February 4, 5 Computer Science IBM Research Report On the Tradeoff among Capacity, Routing Hops, and Being Peer-to-Peer in the Design of Structured Overay Networks Chunqiang Tang, Meissa J.

More information

Privacy Preserving Subgraph Matching on Large Graphs in Cloud

Privacy Preserving Subgraph Matching on Large Graphs in Cloud Privacy Preserving Subgraph Matching on Large Graphs in Coud Zhao Chang,#, Lei Zou, Feifei Li # Peing University, China; # University of Utah, USA; {changzhao,zouei}@pu.edu.cn; {zchang,ifeifei}@cs.utah.edu

More information

Flow-sensitive Alias Analysis

Flow-sensitive Alias Analysis Flow-sensitive Alias Analysis Last time Client-Driven pointer analysis Today Demand DFA paper Scalable flow-sensitive alias analysis March 30, 2015 Flow-Sensitive Alias Analysis 1 Recall Previous Flow-Sensitive

More information

Efficient Parallel Hierarchical Clustering

Efficient Parallel Hierarchical Clustering Efficient Parallel Hierarchical Clustering Manoranjan Dash 1,SimonaPetrutiu, and Peter Scheuermann 1 Deartment of Information Systems, School of Comuter Engineering, Nanyang Technological University, Singaore

More information

On Finding the Best Partial Multicast Protection Tree under Dual-Homing Architecture

On Finding the Best Partial Multicast Protection Tree under Dual-Homing Architecture On inding the est Partia Muticast Protection Tree under ua-homing rchitecture Mei Yang, Jianping Wang, Xiangtong Qi, Yingtao Jiang epartment of ectrica and omputer ngineering, University of Nevada Las

More information

PCT: Partial Co-Alignment of Social Networks

PCT: Partial Co-Alignment of Social Networks PCT: Partia Co-Aignment of Socia Networks Jiawei Zhang University of Iinois at Chicago Chicago, IL, USA jzhan9@uicedu Phiip S Yu University of Iinois at Chicago, IL, USA Institute for Data Science Tsinghua

More information

Introduction to Parallel Algorithms

Introduction to Parallel Algorithms CS 1762 Fall, 2011 1 Introduction to Parallel Algorithms Introduction to Parallel Algorithms ECE 1762 Algorithms and Data Structures Fall Semester, 2011 1 Preliminaries Since the early 1990s, there has

More information

Chapter 5: Transactions in Federated Databases

Chapter 5: Transactions in Federated Databases Federated Databases Chapter 5: in Federated Databases Saes R&D Human Resources Kemens Böhm Distributed Data Management: in Federated Databases 1 Kemens Böhm Distributed Data Management: in Federated Databases

More information

INTEGRATED REGISTRATION OF RANGE IMAGES FROM TERRESTRIAL LIDAR

INTEGRATED REGISTRATION OF RANGE IMAGES FROM TERRESTRIAL LIDAR INEGRAED REGISRAION OF RANGE IMAGES FROM ERRESRIAL LIDAR Wang Yanmin a, Wang Guoi b a Schoo of Geomatics and Urban Information, Beijing University of Civi Engineering and Architecture, Zhananguan Road,

More information

Research on the overall optimization method of well pattern in water drive reservoirs

Research on the overall optimization method of well pattern in water drive reservoirs J Petro Expor Prod Techno (27) 7:465 47 DOI.7/s322-6-265-3 ORIGINAL PAPER - EXPLORATION ENGINEERING Research on the overa optimization method of we pattern in water drive reservoirs Zhibin Zhou Jiexiang

More information

A Near-Optimal Distributed QoS Constrained Routing Algorithm for Multichannel Wireless Sensor Networks

A Near-Optimal Distributed QoS Constrained Routing Algorithm for Multichannel Wireless Sensor Networks Sensors 2013, 13, 16424-16450; doi:10.3390/s131216424 Artice OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journa/sensors A Near-Optima Distributed QoS Constrained Routing Agorithm for Mutichanne Wireess

More information

Binarized support vector machines

Binarized support vector machines Universidad Caros III de Madrid Repositorio instituciona e-archivo Departamento de Estadística http://e-archivo.uc3m.es DES - Working Papers. Statistics and Econometrics. WS 2007-11 Binarized support vector

More information

Hardware-Accelerated Formal Verification

Hardware-Accelerated Formal Verification Hardare-Accelerated Formal Verification Hiroaki Yoshida, Satoshi Morishita 3 Masahiro Fujita,. VLSI Design and Education Center (VDEC), University of Tokyo. CREST, Jaan Science and Technology Agency 3.

More information

file://j:\macmillancomputerpublishing\chapters\in073.html 3/22/01

file://j:\macmillancomputerpublishing\chapters\in073.html 3/22/01 Page 1 of 15 Chapter 9 Chapter 9: Deveoping the Logica Data Mode The information requirements and business rues provide the information to produce the entities, attributes, and reationships in ogica mode.

More information

Response Surface Model Updating for Nonlinear Structures

Response Surface Model Updating for Nonlinear Structures Response Surface Mode Updating for Noninear Structures Gonaz Shahidi a, Shamim Pakzad b a PhD Student, Department of Civi and Environmenta Engineering, Lehigh University, ATLSS Engineering Research Center,

More information

A Comparison of a Second-Order versus a Fourth- Order Laplacian Operator in the Multigrid Algorithm

A Comparison of a Second-Order versus a Fourth- Order Laplacian Operator in the Multigrid Algorithm A Comparison of a Second-Order versus a Fourth- Order Lapacian Operator in the Mutigrid Agorithm Kaushik Datta (kdatta@cs.berkeey.edu Math Project May 9, 003 Abstract In this paper, the mutigrid agorithm

More information

Dynamic Symbolic Execution of Distributed Concurrent Objects

Dynamic Symbolic Execution of Distributed Concurrent Objects Dynamic Symboic Execution of Distributed Concurrent Objects Andreas Griesmayer 1, Bernhard Aichernig 1,2, Einar Broch Johnsen 3, and Rudof Schatte 1,2 1 Internationa Institute for Software Technoogy, United

More information

Register Allocation. Consider the following assignment statement: x = (a*b)+((c*d)+(e*f)); In posfix notation: ab*cd*ef*++x

Register Allocation. Consider the following assignment statement: x = (a*b)+((c*d)+(e*f)); In posfix notation: ab*cd*ef*++x Register Aocation Consider the foowing assignment statement: x = (a*b)+((c*d)+(e*f)); In posfix notation: ab*cd*ef*++x Assume that two registers are avaiabe. Starting from the eft a compier woud generate

More information

S16-02, URL:

S16-02, URL: Self Introduction A/Prof ay Seng Chuan el: Email: scitaysc@nus.edu.sg Office: S-0, Dean s s Office at Level URL: htt://www.hysics.nus.edu.sg/~hytaysc I was a rogrammer from to. I have been working in NUS

More information